Evidence map›Paper›PMID 38332169›Full record

ArticleScientific reports2024

Non-contact assessment of cardiac physiology using FO-MVSS-based ballistocardiography: a promising approach for heart failure evaluation.

Jing Zhan, Xiaoyan Wu, Xuelei Fu, Chenze Li, Ke-Qiong Deng, Qin Wei, Chao Zhang, Tao Zhao, Congcong Li, Longting Huang and 4 more

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
1.5field-weighted citation impact, top 21% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 8 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors at 3 institutions in 1 country.

Jing Zhan *Hubei Key Laboratory of Broadband Wireless Communication and Sensor Networks, School of Information Engineering, Wuhan University of Technology, Wuhan, 430070, Hubei, China.
Xiaoyan Wu *Department of Cardiology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, China.
Xuelei FuHubei Key Laboratory of Broadband Wireless Communication and Sensor Networks, School of Information Engineering, Wuhan University of Technology, Wuhan, 430070, Hubei, China.
Chenze LiDepartment of Cardiology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, China.
Ke-Qiong DengDepartment of Cardiology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, China.
Qin WeiHubei Key Laboratory of Broadband Wireless Communication and Sensor Networks, School of Information Engineering, Wuhan University of Technology, Wuhan, 430070, Hubei, China.
Chao ZhangDepartment of Cardiology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, China.
Tao ZhaoHubei Key Laboratory of Broadband Wireless Communication and Sensor Networks, School of Information Engineering, Wuhan University of Technology, Wuhan, 430070, Hubei, China.
Congcong LiHubei Key Laboratory of Broadband Wireless Communication and Sensor Networks, School of Information Engineering, Wuhan University of Technology, Wuhan, 430070, Hubei, China.
Longting HuangHubei Key Laboratory of Broadband Wireless Communication and Sensor Networks, School of Information Engineering, Wuhan University of Technology, Wuhan, 430070, Hubei, China.
Kewei ChenHubei Key Laboratory of Broadband Wireless Communication and Sensor Networks, School of Information Engineering, Wuhan University of Technology, Wuhan, 430070, Hubei, China.
Qiongxin WangDepartment of Cardiology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, China.
Zhengying LiHubei Key Laboratory of Broadband Wireless Communication and Sensor Networks, School of Information Engineering, Wuhan University of Technology, Wuhan, 430070, Hubei, China. zhyli@whut.edu.cn.
Zhibing LuDepartment of Cardiology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, China. luzhibing222@163.com.
Wuhan University of Technology · CNWuhan University · CNZhongnan Hospital of Wuhan University · CN

Funding

National Natural Science Foundation of China 61735013National Natural Science Foundation of China 82070425
6 · The paper itself

Abstract

Continuous monitoring of cardiac motions has been expected to provide essential cardiac physiology information on cardiovascular functioning. A fiber-optic micro-vibration sensing system (FO-MVSS) makes it promising. This study aimed to explore the correlation between Ballistocardiography (BCG) waveforms, measured using an FO-MVSS, and myocardial valve activity during the systolic and diastolic phases of the cardiac cycle in participants with normal cardiac function and patients with congestive heart failure (CHF). A high-sensitivity FO-MVSS acquired continuous BCG recordings. The simultaneous recordings of BCG and electrocardiogram (ECG) signals were obtained from 101 participants to examine their correlation. BCG, ECG, and intracavitary pressure signals were collected from 6 patients undergoing cardiac catheter intervention to investigate BCG waveforms and cardiac cycle phases. Tissue Doppler imaging (TDI) measured cardiac time intervals in 51 participants correlated with BCG intervals. The BCG recordings were further validated in 61 CHF patients to assess cardiac parameters by BCG. For heart failure evaluation machine learning was used to analyze BCG-derived cardiac parameters. Significant correlations were observed between cardiac physiology parameters and BCG's parameters. Furthermore, a linear relationship was found betwen IJ amplitude and cardiac output (r = 0.923, R

Indexed as

BallistocardiographyHeart FailureElectrocardiographyHeartHumansMyocardial Contraction

Identifiers

PMID38332169
PMCPMC10853251
OpenAlexW4391654795

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.